Applications of RNA Indexes for Precision Oncology in Breast Cancer

Application of data science and bioinformatics in RNA therapeutics

Nowadays, information technology (IT) has been holding a significant role in daily life worldwide. The trajectory of data science and bioinformatics promises pioneering personalized therapies, reshaping medical landscapes and patient care. For RNA therapy to reach more patients, a comprehensive understanding of the application of data science and bioinformatics to this therapy is essential. Thus, this chapter has summarized the application of data science and bioinformatics in RNA therapeutics. Data science applications in RNA therapy, such as data integration and analytics, machine learning, and drug development, have been discussed. In addition, aspects of bioinformatics such as RNA design and evaluation, drug delivery system simulation, and databases for personalized medicine have also been covered in this chapter. These insights have shed light on existing evidence and opened potential future directions. From there, scientists can elevate RNA-based therapeutics into an era of tailored treatments and revolutionary healthcare.

Genetic variations in tumor-suppressor miRNA-encoding genes and their target genes: focus on breast cancer development and possible therapeutic strategies

MicroRNAs (miRNAs) negatively affect gene expression by binding to their specific mRNAs resulting in either mRNA destruction or translational repression. The aberrant expression of various miRNAs has been associated with a number of human cancer. Oncogenic or tumor-suppressor miRNAs regulate a variety of pathways involved in the development of breast cancer (BC), including cell proliferation, apoptosis, metastasis, cancer recurrence, and chemoresistance. Variations in miRNA-encoding genes and their target genes lead to dysregulated gene expression resulting in the development and progression of BC. The various therapeutic approaches to treat the disease include chemotherapy, radiation therapy, surgical removal, hormone therapy, chemotherapy, and targeted biological therapy. The purpose of the current review is to explore the genetic variations in tumor-suppressor miRNA-encoding genes and their target genes in association with the disease development and prognosis. The therapeutic interventions targeting the variants for better disease outcomes have also been discussed.

New Biomarkers and Treatment Advances in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a specific subtype of breast cancer lacking hormone receptor expression and HER2 gene amplification. TNBC represents a heterogeneous subtype of breast cancer, characterized by poor prognosis, high invasiveness, high metastatic potential, and a tendency to relapse. In this review, the specific molecular subtypes and pathological aspects of triple-negative breast cancer are illustrated, with particular attention to the biomarker characteristics of TNBC, namely: regulators of cell proliferation and migration and angiogenesis, apoptosis-regulating proteins, regulators of DNA damage response, immune checkpoints, and epigenetic modifications. This paper also focuses on omics approaches to exploring TNBC, such as genomics to identify cancer-specific mutations, epigenomics to identify altered epigenetic landscapes in cancer cells, and transcriptomics to explore differential mRNA and protein expression. Moreover, updated neoadjuvant treatments for TNBC are also mentioned, underlining the role of immunotherapy and novel and targeted agents in the treatment of TNBC.

Clinical Evidence of Circulating Tumor DNA Application in Aggressive Breast Cancer

Breast cancer is clinically and biologically heterogeneous and is classified into different subtypes according to the molecular landscape of the tumor. Triple-negative breast cancer is a subtype associated with higher tumor aggressiveness, poor prognosis, and poor response to treatment. In metastatic breast cancer, approximately 6% to 10% of new breast cancer cases are initially staged IV (de novo metastatic disease). The number of metastatic recurrences is estimated to be 20-30% of all existing breast tumor cases, whereby the need to develop specific genetic markers to improve the prognosis of patients suffering from these deadly forms of breast cancer. As an alternative, liquid biopsy methods can minutely identify the molecular architecture of breast cancer, including aggressive forms, which provides new perspectives for more precise diagnosis and more effective therapeutics. This review aimed to summarize the current clinical evidence for the application of circulating tumor DNA in managing breast cancer by detailing the increased usefulness of this biomarker as a diagnostic, prognostic, monitoring, and surveillance marker for breast cancer.

Artificial intelligence assists precision medicine in cancer treatment

Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.

New progress in the role of microRNAs in the diagnosis and prognosis of triple negative breast cancer

Triple negative breast cancer is distinguished by its high malignancy, aggressive invasion, rapid progression, easy recurrence, and distant metastases. Additionally, it has a poor prognosis, a high mortality, and is unresponsive to conventional endocrine and targeted therapy, making it a challenging problem for breast cancer treatment and a hotspot for scientific research. Recent research has revealed that certain miRNA can directly or indirectly affect the occurrence, progress and recurrence of TNBC. Their expression levels have a significant impact on TNBC diagnosis, treatment and prognosis. Some miRNAs can serve as biomarkers for TNBC diagnosis and prognosis. This article summarizes the progress of miRNA research in TNBC, discusses their roles in the occurrence, invasion, metastasis, prognosis, and chemotherapy of TNBC, and proposes a treatment strategy for TNBC by interfering with miRNA expression levels.

Identifying and Validating of an Autophagy-Related Gene Signature for the Prediction of Early Relapse in Breast Cancer

BACKGROUND

Compelling evidence has demonstrated the pivotal role of autophagy in the prognosis of breast cancer. Breast cancer (BC) patients with early relapse consistently exhibited worse survival.

METHODS

The autophagy-related genes were derived from the Human Autophagy Database (HADb) and high-sequencing data were obtained from The Cancer Genome Atlas (TCGA). Discrepantly expressed autophagy genes (DEAGs) between early relapse and long-term survival groups were performed using the Linear Models for Microarray data (LIMMA) method. Lasso Cox regression analysis was conducted for the selection of the 4-gene autophagy-related gene signature. GSE42568 and GSE21653 databases were enrolled in this study for the external validation of the signature. Then patients were divided into high and low-risk groups based on the specific score formula. GSEA was used to discover the related signaling pathway. The Kaplan-Meier curves and the receiver operating characteristic (ROC) curves were used to evaluate the discrimination and accuracy of the 4-gene signature.

RESULTS

A signature composed of four autophagy-related mRNA including APOL1, HSPA8, SIRT1, and TP73, was identified as significantly associated with the early relapse in BC patients. Time-dependent receiver-operating characteristic at 1 year suggested remarkable accuracy of the signature [area under the curve (AUC = 0.748)]. The risk score model based on the autophagy-related signature showed favorable predicting value in 1-, 2-, and 3-year relapse-free survival (RFS) in training and two validating cohorts. The GSEA displayed gene sets were remarkably enriched in carcinogenic activation pathways and autophagy-related pathways. The nomogram involving three variables (progesterone receptor status, T stage, and 4-gene signature) exhibited relatively good discrimination with a C-index of 0.766.

CONCLUSIONS

Our study establishes an autophagy-related 4-gene signature that can effectively stratify the high-risk and low-risk BC patients for early relapse. Combined with the clinicopathological variables, the signature could significantly help oncologists tailor more efficient treatment strategies for BC patients.

Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways

Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.

Insight Into the Prospects for RNAi Therapy of Cancer

RNA interference (RNAi), also known as gene silencing, is a biological process that prevents gene expression in certain diseases such as cancer. It can be used to improve the accuracy, efficiency, and stability of treatments, particularly genetic therapies. However, challenges such as delivery of oligonucleotide drug to less accessible parts of the body and the high incidence of toxic side effects are encountered. It is therefore imperative to improve their delivery to target sites and reduce their harmful effects on noncancerous cells to harness their full potential. In this study, the role of RNAi in the treatment of COVID-19, the novel coronavirus disease plaguing many countries, has been discussed. This review aims to ascertain the mechanism and application of RNAi and explore the current challenges of RNAi therapy by identifying some of the cancer delivery systems and providing drug information for their improvement. It is worth mentioning that delivery systems such as lipid-based delivery systems and exosomes have revolutionized RNAi therapy by reducing their immunogenicity and improving their cellular affinity. A deeper understanding of the mechanism and challenges associated with RNAi in cancer therapy can provide new insights into RNAi drug development.

RNA Sequencing in Comparison to Immunohistochemistry for Measuring Cancer Biomarkers in Breast Cancer and Lung Cancer Specimens

RNA sequencing is considered the gold standard for high-throughput profiling of gene expression at the transcriptional level. Its increasing importance in cancer research and molecular diagnostics is reflected in the growing number of its mentions in scientific literature and clinical trial reports. However, the use of different reagents and protocols for RNA sequencing often produces incompatible results. Recently, we published the Oncobox Atlas of RNA sequencing profiles for normal human tissues obtained from healthy donors killed in road accidents. This is a database of molecular profiles obtained using uniform protocol and reagents settings that can be broadly used in biomedicine for data normalization in pathology, including cancer. Here, we publish new original 39 breast cancer (BC) and 19 lung cancer (LC) RNA sequencing profiles obtained for formalin-fixed paraffin-embedded (FFPE) tissue samples, fully compatible with the Oncobox Atlas. We performed the first correlation study of RNA sequencing and immunohistochemistry-measured expression profiles for the clinically actionable biomarker genes in FFPE cancer tissue samples. We demonstrated high (Spearman's rho 0.65-0.798) and statistically significant (p < 0.00004) correlations between the RNA sequencing (Oncobox protocol) and immunohistochemical measurements for HER2/ERBB2, ER/ESR1 and PGR genes in BC, and for PDL1 gene in LC; AUC: 0.963 for HER2, 0.921 for ESR1, 0.912 for PGR, and 0.922 for PDL1. To our knowledge, this is the first validation that total RNA sequencing of archived FFPE materials provides a reliable estimation of marker protein levels. These results show that in the future, RNA sequencing can complement immunohistochemistry for reliable measurements of the expression biomarkers in FFPE cancer samples.

RNA-sequence-based microRNA expression signature in breast cancer: tumor-suppressive miR-101-5p regulates molecular pathogenesis

Aberrantly expressed microRNA (miRNA) are known to disrupt intracellular RNA networks in cancer cells. Exploring miRNA‐dependent molecular networks is a major challenge in cancer research. In this study, we performed RNA‐sequencing of breast cancer (BrCa) clinical specimens to identify tumor‐suppressive miRNA in BrCa. In total, 64 miRNA were identified as candidate tumor‐suppressive miRNA in BrCa cells. Analysis of our BrCa signature revealed that several miRNA duplexes (guide strand/passenger strand) derived from pre‐miRNA were downregulated in BrCa tissues (e.g. miR‐99a‐5p/‐3p, miR‐101‐5p/‐3p, miR‐126‐5p/‐3p, miR‐143‐5p/‐3p, and miR‐144‐5p/‐3p). Among these miRNA, we focused on miR‐101‐5p, the passenger strand of pre‐miR‐101, and investigated its tumor‐suppressive roles and oncogenic targets in BrCa cells. Low expression of miR‐101‐5p predicted poor prognosis in patients with BrCa (overall survival rate: P = 0.0316). Ectopic expression of miR‐101‐5p attenuated aggressive phenotypes, e.g. proliferation, migration, and invasion, in BrCa cells. Finally, we identified seven putative oncogenic genes (i.e. High Mobility Group Box 3, Epithelial splicing regulatory protein 1, GINS complex subunit 1 (GINS1), Tumor Protein D52, Serine/Arginine‐Rich Splicing Factor Kinase 1, Vang‐like protein 1, and Mago Homolog B) regulated by miR‐101‐5p in BrCa cells. The expression of these target genes was associated with the molecular pathogenesis of BrCa. Furthermore, we explored the oncogenic roles of GINS1, whose function had not been previously elucidated, in BrCa cells. Aberrant expression of GINS1 mRNA and protein was observed in BrCa clinical specimens, and high GINS1 expression significantly predicted poor prognosis in patients with BrCa (overall survival rate: P = 0.0126). Knockdown of GINS1 inhibited the malignant features of BrCa cells. Thus, identification of tumor‐suppressive miRNA and molecular networks controlled by these miRNA in BrCa cells may be an effective strategy for elucidation of the molecular pathogenesis of this disease.

The MicroRNA-382-5p/MXD1 Axis Relates to Breast Cancer Progression and Promotes Cell Malignant Phenotypes

BACKGROUND

MicroRNA (miRNA)-382-5p functions as an oncogenic miRNA in breast cancer. MXD1 was demonstrated to be one of its direct targets. However, the involvement of miRNA-382-5p/MXD1 axis in breast cancer remains unknown. The aim of this study was to investigate the expression pattern, clinical significance, and potential functions of miRNA-382-5p/MXD1 axis in breast cancer.

MATERIALS AND METHODS

Quantitative polymerase chain reaction was performed to detect the expression levels of miRNA-382-5p and MXD1 messenger RNA (mRNA) in 96 pairs of breast cancer and matched noncancerous breast tissue samples from the same patients. Relationships between miRNA-382 expression, MXD1 expression, and combined miRNA-382-5p and MXD1 expression, and various clinicopathological characteristics of breast cancer were statistically evaluated, and their roles in breast cancer cell proliferation and invasion were also examined.

RESULTS

Compared with noncancerous breast tissues, miRNA-382-5p expression was upregulated but MXD1 mRNA expression was downregulated in breast cancer tissues (both P < 0.01). High miRNA-382 expression, MXD1 expression, and combined miRNA-382-5p and low MXD1 expression were significantly associated with advanced tumor stage and the presence of lymph node metastasis (all P < 0.05). Overexpression of miRNA-382-5p dramatically reduced MXD1 mRNA and protein expression levels in breast cancer cells. miRNA-382-5p upregulation markedly enhanced breast cancer cell proliferation and invasion, while its downregulation inhibited these malignant phenotypes of breast cancer cells in vitro. Notably, overexpressed MXD1 reversed the effects of upregulated miRNA-382-5p on cell proliferation and invasion in vitro.

CONCLUSIONS

The dysregulation of miRNA-382-5p-MXD1 axis may be involved in the development and aggressive progression of breast cancer. miRNA-382-5p may target MXD1, leading to cell invasion and proliferation in breast cancer cells in vitro, implying its potentials as a therapeutic target for this type of cancer.

RNA sequencing for research and diagnostics in clinical oncology

Molecular diagnostics is becoming one of the major drivers of personalized oncology. With hundreds of different approved anticancer drugs and regimens of their administration, selecting the proper treatment for a patient is at least nontrivial task. This is especially sound for the cases of recurrent and metastatic cancers where the standard lines of therapy failed. Recent trials demonstrated that mutation assays have a strong limitation in personalized selection of therapeutics, consequently, most of the drugs cannot be ranked and only a small percentage of patients can benefit from the screening. Other approaches are, therefore, needed to address a problem of finding proper targeted therapies. The analysis of RNA expression (transcriptomic) profiles presents a reasonable solution because transcriptomics stands a few steps closer to tumor phenotype than the genome analysis. Several recent studies pioneered using transcriptomics for practical oncology and showed truly encouraging clinical results. The possibility of directly measuring of expression levels of molecular drugs' targets and profiling activation of the relevant molecular pathways enables personalized prioritizing for all types of molecular-targeted therapies. RNA sequencing is the most robust tool for the high throughput quantitative transcriptomics. Its use, potentials, and limitations for the clinical oncology will be reviewed here along with the technical aspects such as optimal types of biosamples, RNA sequencing profile normalization, quality controls and several levels of data analysis.

Overexpression of Tumor Protein p53-regulated Apoptosis-inducing Protein 1 Regulates Proliferation and Apoptosis of Breast Cancer Cells through the PI3K/Akt Pathway

Purpose

Tumor protein p53-regulated apoptosis-inducing protein 1 (TP53AIP1) functions in various cancers. We studied the effect and molecular mechanism of TP53AIP1 in breast cancer.

Methods

The degree of correlation between TP53AIP1 expression and overall survival in patients with breast cancer was obtained from the online The Cancer Genome Atlas database. Six of the TP53AIP1 levels in the tumor and adjacent non-tumor tissues randomly selected from 38 breast cancer patients were determined. Transgenic technology was used to enhance the expression of TP53AIP1 in breast cancer cell lines, MDA-MB-415 and MDA-MB-468, and to observe the effects of gene overexpression on the proliferation, cell cycle, and apoptosis of breast cancer cells. The molecular mechanism of association between cell cycle- and apoptosis-related factors and the phosphoinositide 3-kinases/protein kinase B (PI3K/Akt) pathway was also studied.

Results

The messenger RNA and protein expression levels of TP53AIP1 in cancer tissues were significantly lower than those in the control group. TP53AIP1 overexpression inhibits cell viability. The mechanism of TP53AIP1 inhibition of proliferation and growth of breast cancer cells includes cell cycle arrest, apoptosis promotion (p < 0.01), promotion of the expression of cleaved-caspase-3 (p < 0.01), cleaved-caspase-9 (p < 0.01), B cell lymphoma/leukemia-2 (Bcl-2)-associated X protein, and p53 (p < 0.01), and the inhibition of Bcl-2, Ki67, and PI3K/Akt pathways (p < 0.01).

Conclusion

TP53AIP1 may be a novel tumor suppressor gene in breast cancer and can potentially be used as an effective target gene for the treatment of breast cancer.